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What is OpenCV Library?

OpenCV, short for Open Source Computer Vision Library, is an open-source computer vision and machine learning software library. Originally developed by Intel, it is now maintained by a community of developers under the OpenCV Foundation.

In this article, we delve into OpenCV, exploring its functionalities, applications, and practical examples.

OpenCV- Introduction

Opencv is a huge open-source library for computer vision, machine learning, and image processing. Now, it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human.

When it is integrated with various libraries, such as NumPy, python is capable of processing the opencv array structure for analysis. To Identify an image pattern and its various features we use vector space and perform mathematical operations on these features. 

The first OpenCV version was 1.0. OpenCV is released under a BSD license and hence it’s free for both academic and commercial use. It has C++, C, Python, and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. When opencv was designed the main focus was real-time applications for computational efficiency. All things are written in optimized C/C++ to take advantage of multi-core processing. 

You can also refer to – OpenCV Tutorial to gain in-depth knowledge of opencv.

Example of OpenCV

Let us consider a basic example to illustrate the various capabilities of OpenCV. Let take an example of the image mentioned below .

OpenCV allows you to perform various operations in the image.

From the above original image, lots of pieces of information that are present in the original image can be obtained. Like in the above image there are two faces available and the person(I) in the images is wearing a bracelet, watch, etc. So with the help of OpenCV we can get all these types of information from the original image. 

Applications of OpenCV

There are lots of applications which are solved using OpenCV, some of them are listed below:

OpenCV Functionality 

Image-Processing

Image processing is a method to perform some operations on an image, in order to get an enhanced image and or to extract some useful information from it. 
If we talk about the basic definition of image processing then “Image processing is the analysis and manipulation of a digitized image, especially in order to improve its quality”. 

Digital-Image

An image may be defined as a two-dimensional function f(x, y), where x and y are spatial(plane) coordinates, and the amplitude of fat any pair of coordinates (x, y) is called the intensity or grey level of the image at that point. 
In another words, an image is nothing more than a two-dimensional matrix (3-D in case of coloured images) which is defined by the mathematical function f(x, y) at any point is giving the pixel value at that point of an image, the pixel value describes how bright that pixel is, and what colour it should be. 
Image processing is basically signal processing in which input is an image and output is image or characteristics according to requirement associated with that image. 
Image processing basically includes the following three steps: 

  1. Importing the image
  2. Analysing and manipulating the image
  3. Output in which result can be altered image or report that is based on image analysis

How does a computer read an image? 

Computers don’t “see” images in the way humans do. Instead, they interpret images as arrays of numerical values. The basic process of how a computer reads and processes an image are:

  1. Pixel Values: An image is made up of pixels, which are the smallest units of information in an image. Each pixel has a value that represents its color and intensity. In the case of an RGB image, there are three values for each pixel corresponding to the Red, Green, and Blue channels.
  2. Digital Representation: The RGB values are usually represented as integers ranging from 0 to 255. 0 represents the absence of color (black), and 255 represents the maximum intensity of that color (full brightness).
  3. Image Matrix: The computer reads the image as a matrix of numbers, where each element in the matrix corresponds to the pixel value at that location. For a color image, there are typically three matrices, one for each RGB channel.
  4. Image Processing: Image processing algorithms are applied to manipulate these numerical representations. Common operations include resizing, cropping, filtering, and more.

Conclusion

In this article the main points of opencv are explored such it’s importance and How it is effecting the world constantly. It will help developers to know the capabilities of opencv projects nad applications.
 


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